Cyclic variations of variables are ubiquitous in biomedical science. A number of methods for detecting rhythms have been developed, but they are often difficult to interpret. A simple procedure for detecting cyclic variations in biological time series and quantification of their probability is presented here. Analysis of rhythmic variance (ANORVA) is based on the premise that the variance in groups of data from rhythmic variables is low when a time distance of one period exists between the data entries. A detailed stepwise calculation is presented including data entry and preparation, variance calculating, and difference testing. An example for the application of the procedure is provided, and a real dataset of the number of papers published per day in January 2003 using selected keywords is compared to randomized datasets. Randomized datasets show no cyclic variations. The number of papers published daily, however, shows a clear and significant (p<0.03) circaseptan (period of 7 days) rhythm, probably of social origin